Protein content in milk of holstein black-and-white cows
Bibliographic record
Abstract
Abstract The most optimal method to solve the problem for cow’s milk and protein content increase is to carry out zootechnical and breeding activities. The goal was to study the milk protein structure and content of the Holsteinized black-and-white breed cows of different genotypes, as well as to establish the relationship nature between the activity of transamination enzymes and the milk protein content of cows. For this purpose, 4 groups of experimental cows (15 heads in each) were formed according to the principle of father’s belonging to the countries of origin (daughters of seed bulls of Canadian, Danish selection, Dutch and domestic selection). The superiority of cows-daughters of foreign breeding bulls in terms of milk protein content was established. Moreover, the milk of cows born from foreign producers contents high level of casein - the most important fraction from the technological point of view. Electrophoretic analysis of milk proteins isolated 16 fractions, including 9 casein and 7 whey ones. The highest content was found in such fractions as αs1-, β-, αs2-, κ-caseins and β-Lg. The calculated correlation coefficients between the alanine aminotransferase and aspartate aminotransferase and the milk protein activity of cows showed a direct relationship between them in cows of the studied groups. This is a favorable factor for increasing the cows milk protein content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".